Abstract
In this article we propose and evaluate a method to extract terminological relationships from microblogs. The idea is to analyze archived microblogs (tweets for example) and then to trace the history of each term. Similar history indicates a relationship between terms. This indication can be validated using further processing. For example, if the term t1 and t2 were frequently used in Twitter at certain days, and there is a match in the frequency patterns over a period of time, then t1 and t2 can be related. Extracting standard terminological relationships can be difficult; especially in a dynamic context such as social media, where millions of microblogs (short textual messages) are published, and thousands of new terms are coined every day. So we are proposing to compile nonstandard raw repository of lexical units with unconfirmed relationships. This paper shows a method to draw relationships between time-sensitive Arabic terms by matching similar timelines of these terms. We use dynamic time warping to align the timelines. To evaluate our approach we elected 430 terms and we matched the similarity between the frequency patterns of these terms over a period of 30 days. Around 250 correct relationships were extracted with a precision of 0.65. These relationships were drawn without using any parallel text, nor analyzing the textual context of the term. Taking into consideration that the studied terms can be newly coined by microbloggers and their availability in standard repositories is limited.
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Daoud, D., Daoud, M. (2018). Extracting Terminological Relationships from Historical Patterns of Social Media Terms. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_14
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